Presently, a number of intensity modulation techniques are available, including fixed gantry IMRT planned using beamlet-based optimization (BBO) or direct aperture optimization (DAO), volumetric modulated arc therapy (VMAT), tomotherapy delivery (slice-by-slice delivery, binary collimator for modulation of beamlet intensity).
Each of these methods captures some aspect(s) of desired features of RT, but compromises in either dose distribution (fixed-gantry IMRT or VMAT) or inefficient delivery (tomotherapy). A truly optimal form of RT fully utilizing the technical capability of intensity modulation with efficient delivery is yet to be developed.
In intensity-modulated radiation therapy (IMRT), the treatment plan is selected from a large pool of physically feasible solutions by optimization of an objective function. The final solution depends on the choice of objective function and constraints applied to the optimization. Two commonly used approaches are beamlet-based and segment-based optimizations. In the traditional beamlet-based algorithms for the step and shoot IMRT, each beamlet intensity is an independent and continuous variable. For a fast calculation, the nonconvex physical constraints of the dose delivery are not included in the optimization. As a result, the optimized beamlet intensity map has a high complexity, and the number of segments for dose delivery is usually large after leaf sequencing. A large number of segments reduce not only treatment efficiency but also treatment accuracy due to increased patient motion during beam delivery and the involvement of irregularly shaped segments. Many attempts have been made to reduce the fluence map complexity by using various data smoothing techniques. These algorithms smooth the edges and help get rid of spiky behaviors of fluence maps. However, the overall shapes of the final fluence maps remain the same and, as thus, the solution so obtained represents only a small perturbation to the original unsmoothed plan and the reduction of the number of segments is usually rather limited.
Segment-based methods tackle the problem from the delivery aspect typically by enforcing a prechosen (often unjustified) number of segments for each incident beam and then optimizing the shapes and weights of the apertures. However, searching for an optimal solution by using segment based optimization is inherently complicated because of the highly nonconvex dependence of the objective function on the multi-leaf collimator (MLC) coordinates and the optimality of the final solution is not always guaranteed when an iterative algorithm is used.
An important characteristic that has not been utilized in most of inverse planning methods is that the IMRT solution space is highly degenerated in the sense that there are usually a large number of IMRT plans for the same prescription. While these plans yield similar dose distributions satisfying the prescription and constraints, the fluence maps of the plans can be dramatically different. Therefore, it is possible to stipulate constraints in the search of the optimal beamlet intensity such that the resultant number of segments is greatly reduced while the dose distribution is not severely deteriorated.
What is needed is, instead of directly including the nonconvex physical constraints in the optimization, which is computationally intensive and increases the probability of being trapped in local optimal solutions, an efficient method to achieve a global optimal solution only in a sparse space of fluence maps where the physical constraints are implied. What is further needed is a method that uses a beamlet intensity map delivered using a small number of segments that are piecewise constant and its derivative is sparse.